python-multiprocessing - w3toppers.com

How to terminate long-running computation (CPU bound task) in Python using asyncio and concurrent.futures.ProcessPoolExecutor?

How do I terminate such long running CPU-bound computations within a method? The approach you tried doesn’t work because the futures returned by ProcessPoolExecutor are not cancellable. Although asyncio’s run_in_executor tries to propagate the cancellation, it is simply ignored by Future.cancel once the task starts executing. There is no fundamental reason for that. Unlike threads, … Read more

cProfile causes pickling error when running multiprocessing Python code

The problem you’ve got here is that, by using -mcProfile, the module __main__ is cProfile (the actual entry point of the code), not your script. cProfile tries to fix this by ensuring that when your script runs, it sees __name__ as “__main__”, so it knows it’s being run as a script, not imported as a … Read more

How to limit number of CPU’s used by a python script w/o terminal or multiprocessing library?

I am looking for a way to limit a python scripts CPU usage (not priority but the number of CPU cores) with python code. Run you application with taskset or numactl. For example, to make your application utilize only the first 4 CPUs do: taskset –cpu-list 0-3 <app> These tools, however, limit the process to … Read more

How can I share a class between processes?

multiprocessing.Value isn’t designed to be used with custom classes, it’s supposed to be similar to a multiprocessing.sharedctypes.Value. Instead, you need to create a custom manager and register your class with it. Your life will also be easier if you don’t access value directly, but modify/access it via methods, which will get exported by the default … Read more

How can I abort a task in a multiprocessing.Pool after a timeout?

Here’s a way you can do this without needing to change your worker function. There are two steps required: Use the maxtasksperchild option you can pass to multiprocessing.Pool to ensure the worker processes in the pool are restarted after every task execution. Wrap your existing worker function in another function, which will call worker in … Read more

Multiprocessing: use only the physical cores?

I know the topic is quite old now, but as it still appears as the first answer when typing ‘multiprocessing logical core’ in google… I feel like I have to give an additional answer because I can see that it would be possible for people in 2018 (or even later..) to get easily confused here … Read more

Understanding Multiprocessing: Shared Memory Management, Locks and Queues in Python

multiprocessing.Lock is implemented using a Semaphore object provided by the OS. On Linux, the child just inherits a handle to the Semaphore from the parent via os.fork. This isn’t a copy of the semaphore; it’s actually inheriting the same handle the parent has, the same way file descriptors can be inherited. Windows on the other … Read more

Understanding Multiprocessing: Shared Memory Management, Locks and Queues in Python

Python multiprocessing application is getting stuck in docker container

Two approaches: try concurrent.futures process executor Different libraries handle cases like these differently. Might fail more gracefully under your specific circumstances. psutils and manual memory allocation logging If you are building this into a production process, this is a must given errors you’ve encountered so far.

Creating and updating nested dictionaries and lists inside multiprocessing.Manager object

Your usage of the shared dictionary is identical to and can be re-written as this: temp = shared_object[‘nested’] temp.update({‘second_key’: ‘second_value’}) When you request the value of the nested key from the shared dictionary, it is stored in the current process’s memory space. This means that any changes you do to this dictionary will not be … Read more